hpmcm.shear_data module

class hpmcm.shear_data.ShearData(shear_table, stats_table, shear, cat_type, tract, snr_cut=7.5)[source]

Bases: object

Collection of shear related data for a single catalog

Attritubes

shear: float

Applied shear

cat_type: str

Catalog type

tract: int

Tract

n_objects: int

Number of objects in catalog

n_in_cell: int

Nubmer of objects in the central region of cell

n_used: int

Number of objects passing snr cut and in central region of cell

n_good: int

Number of fully matched objects

n_bad: int

Number on non-fully matched objects

n_all: int

Number of fully and non-fully matched objects

effic: float

Efficiency to fully match objects

efficErr: float

Error on efficiency to fully match objects

hists: ShearHistograms

Histograms of shear data

stats: ShearStats

Summary statistics

classmethod load(filepath)[source]

Load from a pickle file

Return type:

ShearData

Parameters:

filepath (str)

makePlots(*, use_central=True)[source]

Make the standard plots

Return type:

dict[str, Figure]

Parameters:

use_central (bool)

save(filepath)[source]

Save to a pickle file

Return type:

None

Parameters:

filepath (str)

savefigs(output_file_base)[source]

Save all the figures

Return type:

None

Parameters:

output_file_base (str)

toDict()[source]

Convert self to a dict

Return type:

dict

Parameters:

  • shear_table (pandas.DataFrame)

  • stats_table (pandas.DataFrame)

  • shear (float)

  • cat_type (str)

  • tract (int)

  • snr_cut (float)

class hpmcm.shear_data.ShearHistogramStats(weights, bin_centers)[source]

Bases: object

Simple class to store stats about a histogram

Parameters:

  • weights (np.ndarray)

  • bin_centers (np.ndarray)

w

Sum of weights

Type:

float

mean

Histogram mean

Type:

float

std

Histogram standard deviation

Type:

float

error

Error on histogram mean

Type:

float

inv_var

Inverse Variance

Type:

float

class hpmcm.shear_data.ShearHistograms(good, bad, cat_type)[source]

Bases: object

Simple class to store histogram relating to shear calibration

{type} is the matching type, one of “good”, “bad”, “all”

{i}, {j} are the components of the shear: 1, 2

Parameters:

  • good (pandas.DataFrame)

  • bad (pandas.DataFrame)

  • cat_type (str)

bin_edges

Bin edges for all histograms

Type:

np.ndarray

bin_centers

Bin centers for all histograms

Type:

np.ndarray

central

Slice to select central region of histogram

Type:

slice

central_edges

Slice to select edges for central region of histogram

Type:

slice

good_delta_g_{i}_{j}

Histogram of g_{i}_{j}p - g_{i}_{j}m for all well-matched objects

Type:

np.ndarray

{type}_g_{i}_{cat}

Histogram of all g_{i} value of all objects of {type} in {cat}

Type:

np.ndarray

plotMetaDetect(hist1p, hist1m, hist2p, hist2m, stats_g_1=None, stats_g_2=None, shear=0.01, title='', *, use_central=True)[source]

Plot hist1p - hist1m and hist2p - hist2m for all objects of a particular type

Return type:

Figure

Parameters:

  • hist1p (ndarray)

  • hist1m (ndarray)

  • hist2p (ndarray)

  • hist2m (ndarray)

  • stats_g_1 (ShearHistogramStats | None)

  • stats_g_2 (ShearHistogramStats | None)

  • shear (float)

  • title (str)

  • use_central (bool)

plotMetaDetectAll(stats_g_1=None, stats_g_2=None, shear=0.01, *, use_central=True)[source]

Plot hist1p - hist1m and hist2p - hist2m for all objects

Return type:

Figure

Parameters:
plotMetaDetectBad(stats_g_1=None, stats_g_2=None, shear=0.01, *, use_central=True)[source]

Plot hist1p - hist1m and hist2p - hist2m for non-fully matched objects

Return type:

Figure

Parameters:
plotMetaDetectGood(stats_g_1=None, stats_g_2=None, shear=0.01, *, use_central=True)[source]

Plot hist1p - hist1m and hist2p - hist2m for all fully matched objects

Return type:

Figure

Parameters:
plotMetacalib(stats_g_1=None, stats_g_2=None, shear=0.01, *, use_central=True)[source]

Plot delta_g_1_1 and delta_g_2_2 for fully matched objects

Return type:

Figure

Parameters:
class hpmcm.shear_data.ShearProfileHistogramStats(hist_2d)[source]

Bases: object

Simple class to store stats about a 2d histogram

Parameters:

hist_2d (tuple[np.ndarray, np.ndarray, np.ndarray])

w

Sum of weights

Type:

np.array

mean

Histogram mean

Type:

np.array

std

Histogram standard deviation

Type:

np.array

error

Error on histogram mean

Type:

np.array

inv_var

Inverse Variance

Type:

np.array

class hpmcm.shear_data.ShearStats(hists)[source]

Bases: object

Simple class to store shear statisitics

{type} is the matching type, one of “good”, “bad”, “all”

{i}, {j} are the components of the shear: 1, 2

Parameters:

hists (ShearHistograms)

delta_g_{i}_{i}

Stats for g_{i}_{j}p - g_{i}_{j}m for fully matched objects

Type:

ShearHistogramStats

{type}_g_{i}_{j}

Stats for g_{i}_{j} for objects of {type}

Type:

ShearHistogramStats